Machine learning brings us cool innovations like self-driving cars, online shopping assistants/recommendations, and fraud detection. However, every amazing new tech needs a little help before it becomes fully realized. And that’s why we’re discussing pattern recognition.
This article discusses pattern recognition and machine learning. We will go over what it is, why it’s important, its applications and techniques, and give some examples and pattern recognition and machine learning algorithms. We are also introducing a collection of mock answers for the pattern recognition test.
When you finish this article, you will gain a greater understanding and appreciation for pattern recognition and its role in machine learning. That’s valuable knowledge to have in this world of ever-increasing smart machines.
What is Pattern Recognition and Machine Learning?
In the human brain (which Artificial Intelligence and machine learning seek to emulate), pattern recognition is the cognitive process that happens in the brain when it matches the information that we see with the data stored in our memories.
When we’re talking about computer science, however, pattern recognition is the technology that matches incoming data with information stored in a database. Thus, pattern recognition is a type of machine learning since it uses machine learning algorithms to recognize patterns.
Pattern recognition and machine learning detect arrangements of characteristics of data that uncover information about a given data set or system and is characterized by these four qualities:
- It learns from data
- It automatically recognizes patterns even if partially visible
- It can recognize familiar patterns
- The recognition comes from different shapes and angles
In other words, pattern recognition and machine learning are two sides of the same coin.
Let’s round out our pattern recognition definition with this helpful diagram provided by Huspi.com. It outlines a basic pattern recognition system.
Machine Learning: A Reminder
Machine learning is a type of data analysis that automates analytical model building. Machine learning is a subset of artificial intelligence, based on the concept that systems can learn from data, spot patterns, and reach decisions with little or no human intervention.
Today’s digital world benefits from a host of machine-learning applications. Since pattern recognition is an engineering application of machine learning, it can further enhance its usefulness.
Here’s a handy chart that best illustrates the difference between the two separate but intertwined concepts of pattern recognition and machine learning:
| Machine Learning | Pattern Recognition |
| Method of data analysis that automates analytical model building. | An engineering application of various algorithms used to recognize data patterns. |
| Leans more on the practical side | Leans more on the theoretical side |
| Can be a solution to real-time problems | Can be a real-time problem |
| Requires computers/machines to apply algorithms | Can exist outside of machines |
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The Importance of Pattern Recognition
Pattern recognition tries to duplicate the human brain’s neural network capabilities, which further advances Artificial Intelligence. Pattern recognition is considered one of four cornerstones that make up computer science.
Many practical, computer science-related problems need pattern recognition to help come up with a solution. Pattern finding is the essence of wisdom since patterns embody structure and order, which helps organize our work, making it more accessible. Finding and understanding patterns is a crucial element of problem-solving and mathematical thinking.
There are other reasons why pattern recognition matters, including:
- It identifies and predicts even the tiniest bits of hidden or untraceable data
- It helps classify unseen data
- It makes valuable predictions, employing learning techniques
- It can recognize and identify an object at various distances
- It can help create predictions of unseen data and aids in making practical, actionable suggestions
Pattern Recognition and Machine Learning Techniques
There are three distinct pattern recognition and machine learning models or techniques:
- Statistical Pattern Recognition. This pattern recognition model involves historical statistical data that learns from examples. The model collects observations and processes them. The model then learns to generalize, applying the rules to new observations.
- Syntactic Pattern Recognition. This model is also known as structural pattern recognition because it depends on less complex sub-patterns called primitives. Words, for example, fall into this classification. The pattern is described as connections between the primitives. For instance, words (primitives) connect to form sentences and texts.
- Neural Pattern Recognition. This model relies on artificial neural networks. The networks learn complex nonlinear input-output relations and adapt to the data. This model involves sizable parallel computing systems made up of many simple processors and their interconnections. They can learn complex nonlinear input-output relations, employ sequential training procedures, then adapt themselves to the data.
There are two stages to pattern recognition and machine learning:
- Explorative Stage. The algorithm searches for patterns
- Descriptive Stage. The algorithm categorizes found patterns
Pattern recognition can be anything:
- Text or words
- Images
- Emotions/Sentiments
- Sounds
- Miscellaneous information or other elements
The recognition process follows these steps:
- Gathering the data
- Preprocessing and cleaning it from the noise
- Examining the data and searching for relevant features and common elements
- Clustering and classifying the date
- Performing analysis to gain insights
- Extracting the insights and implementing them in practice
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Pattern Recognition and Machine Learning Applications
Pattern recognition and machine learning is a versatile practice that has found its way into many different industries and social contexts. Here’s a small sampling of how pattern recognition and machine learning is applied today:
- Computer vision: Pattern recognition can recognize single objects in images. Pattern recognition can extract specific patterns from images or videos then used them for face recognition, comparing the new patterns with millions of other images stored in the database. Pattern recognition gives machines human recognition intelligence, which image processing tasks require.
- Civil administration: Surveillance and traffic analysis systems use pattern recognition to identify cars, trucks, or buses.
- Engineering: Popular systems like Alexa, Google Now, and Siri heavily use speech recognition.
- Fingerprint Scanning: Organizations use pattern recognition to identify people for things like tracking event attendance. However, you can find a more straightforward form of scanning, most likely within arm’s reach. Most smartphones, laptops, and tablets have fingerprint locks. Pattern recognition handles the unlocking authorization chore!
- Geology: Geologists can use pattern recognition to detect and identify specific types of rocks and minerals. Experts can also use the pattern recognition and machine learning approach to find, image, and interpret temporal patterns in seismic array recordings to produce different types of seismic analysis models.
- Medical Diagnosis. By using biometric pattern recognition, doctors can spot cancer growth with greater ease and accuracy.
- Speech Recognition: Words are treated as patterns and are widely used in speech recognition algorithms (see Engineering above).
- Stock Market Analysis. The stock market is famously unpredictable and volatile. However, there are still patterns to be recognized and taken advantage of. Applications like Blumberg, Kosho, SofiWealth, and Tinkoff use Artificial Intelligence, backed by pattern recognition and machine learning, to provide financial consultation.
Pattern Recognition Examples and Algorithms
There are two distinct pattern recognition and machine learning algorithms.
- Supervised Algorithms. Supervised algorithms are also called classification. This algorithm uses a two-stage methodology for pattern identification. The first stage covers the model’s development and construction. The second stage encompasses predicting new or unseen objects.
- Unsupervised Algorithms. Unsupervised algorithms favor a “group by” approach. These algorithms observe data patterns and group them according to similarity, such as by dimensions, to make predictions.
When you talk about pattern recognition examples in the human brain, you don’t have to look further than your laundry basket. You employ pattern recognition to sort out the clean socks and place them in their pairs. For something more technical, consider facial recognition. Your eyes, ears, mouth, and nose are known facial features. When you group these features, they create a features vector. This vector helps facial recognition software to search for and identify new data, comparing it to previously stored feature vectors.
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Presenting a Pattern Recognition Test
Pattern recognition tests can give you a better understanding of how machine learning-oriented pattern recognition works. Here are several pattern completion tests courtesy of Indiabix. Give them a try, and picture how a machine could do this job too!
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